Neural circuits incorporate functional activity over a wide range of spatial and temporal scales. Groups of neurons associated with any given task or cognitive operation are typically distributed over different areas. To investigate brain mechanisms related to a specific behavior or cognitive process, it is essential to monitor neuronal activities over various regions of the brain at multiple time scales. In the prior art, microelectrode arrays (MEAs) are widely used to measure neural activities because of their high temporal resolution and their accessibility to various structures of the brain. However, MEAs have significant limitations in scalability and are not ideal for simultaneous high-density, large-area recording at the resolution of single neurons.
Microelectrode arrays (MEAs) detect neuron spikes and measure the local field potential (LFP) generated and are commonly used to monitor neural brain activities because of the high temporal resolution (<1 ms) they provide. However, the large number of monitoring sites required for whole brain recording presents significant challenges in implementing MEA-based systems, such as in vivo integration, power requirements, energy dissipation, and signal transmission and processing. For example, recording spikes of all neurons in a mouse brain using prior art MEA metal electrodes would require ˜7.5×106 sensing electrodes, assuming a signal-to-noise-ratio (SNR) of <100 and a maximum recording distance (rmax) of 130 μm, as described in Reference 1 below. Reference 1 and References 2-49 are listed below and are incorporated by reference.
One of the largest scale MEAs reported to date consists of eight silicon (Si) neural probes (shanks) fabricated with 32 electrodes on each Si probe, totaling 256 signal channels, as described in Reference 2 below. While the Si probes provide access to deep brain layers, enabling investigation of interactions of multiple brain regions, the Si probes are invasive and induce inflammatory responses from glial cells, as described in Reference 3 below. In addition, the inter-probe distance of 300 μm and the inter-electrode distance of 50 μm on each Si probe reported for the 256-channel MEA are not an optimal solution for spike sorting, necessitating higher density, larger scale electrode arrays. Furthermore, the extracranial headstage for MEA backend electronics is large—the printed circuit is 3.4 cm×3.9 cm×2.5 mm, heavy and connected with long wires to the main control electronics, which is not ideal for studying neural activities of freely moving small rodents.
A wireless implantable system-on-a-chip for neural recording and stimulation has been demonstrated with 64 neural recording channels and 64 neural stimulation channels, and used for validating epilepsy treatment, as described in Reference 4 below. Built with standard CMOS technology, the 12 mm2 system-on-a-chip is not biocompatible and is reported to have dissipation power of 1.4 mW for recording and 1.5 mW for stimulation, which limits its scalability for in vivo applications. A wireless, implantable platform for neural activity monitoring has been reported with a 100-element silicon-based MEA, as described in References 5 and 6 below. The reported dissipation power of the wireless platform is 100 mW, and the platform is powered with a Li battery, which requires recharging every eight hours and requires liquid cooling. This results in the implantable wireless platform not being scalable and not being suitable for large-area brain recording.
A proof-of-concept demonstration for embeddable Neural Dust has also been reported, as described in Reference 7 below. Scaling neural dust to useful quantities is not realistic: deployment via the capillary network becomes unrealistic and analysis of many free-floating data sources would be very challenging.
While the neural activity monitoring systems mentioned above are fabricated on rigid surfaces, MEAs fabricated on flexible polymer substrates, such as polyimide, polydimethylsiloxane (PDMS), and parylene, have also been explored for neural activity recording and stimulation devices, as described in References 8, 9 and 10 below. MEAs consisting of a 4×8 electrode array and made of poly(3,4-ethylenedioxythiophene (PEDOT)-carbon nanotube (CNT)-coated microelectrodes (200×200 μm size, 400 μm pitch) have been used to measure the local field potentials (LFPs) of the rat somatosensory cortex, as described in Reference 8 below. The scaling limitations of PEDOT-CNT-coated MEAs is similar to those of conventional metal-based MEAs. Due to their ultra-flexible nature, PEDOT-CNT MEAs are optimal for recording neural activities from the cortex surface, but has limited applicability for depth probing.
Table 1 compares prior art brain neural sensors, including an organic electrochemical transistor (OECT) sensor, as described in Reference 11 below, and shows the key challenges of brain monitoring. The prior art sensors can support the required signal to noise ratio (SNR); however, the prior art sensors have a direct current (DC) power consumption that is well over a brain limit of 40 mW/cm2, which is a limit set so to not raise a local brain temperature and disturb neural activities.
TABLE 1NEUROELECTRIC SENSOR COMPARISON OF PRIORART TO PRESENT DISCLOSURE VERSUS BRAIN CONSTRAINTGrapheneOrganicBrainsensor ofTransis-Con-the presenttorMetalBrain Sensor Specificationstraint1)inventionsensorelectrodesDimension (μm2)50 × 50<20 × 20<20 × 20Max. Power consumption1~1~80(μW)Signal to Noise ratio (1 mV—~30 dB32 dB13 dBspike)Noise floor (@ 10 kHz—<30 nA21 nA2.1 mVreading)Speed~1 KHz~GHz~1 kHzWireless (high data rate)—YesNoNoFlexibility, Bio-desiredYes, YesYes , YesNo, Yescompatibility1)Total power dissipation of 40 mW/cm2 is used so not to raise the local brain temperature and disturb neural activities.
Brain monitoring electronics empowered with high-spatiotemporal electrical recording of neural activity offers a transformative capability to understanding the brain and possible cures for degenerative brain diseases. Action potentials (spikes) have average durations of =2 msec with an average repetition rate of 0.5 Hz-1 kHz, as described in References 12 and 13 below. With human brain neuron densities of 8×1010/1200 cm3, the total data rate from a human brain is about 800 Tbits/sec or 670 Gbits/sec/cm3 with a 10 kHz sampling rate, which oversamples neural signals to enable sorting of neural spikes.
Although the required data rate for the high-density sensor is at least 100× lower than existing modern short-range wireless links, for example, 1 Mbit/sec for the 2.4 GHz low-power Bluetooth IEEE 802.15.1 standard, brain interfacing radio frequency (RF) electronics and wireless links have yet to be developed. Key challenges include: (1) meeting an ultra-low-power budget that is scalable to a whole brain interfacing capability, (2) low bit error and high data rate communication, (3) long-term power management, (4) small chip size to prevent damage to the brain, and (5) flexible and biocompatible with congruent contact to the corrugated brain surfaces, as shown in the brain constraints of Table 2.
TABLE 2Wireless specifications for brain neural activity recording compared to present disclosure and prior art.Brain Innovations & Comparison to SOAinterfacingBrainGHz graphenePenta-65 nm180Wireless linkCon-of the presentceneCMOSnmSpecificationstraint1inventionFETSRFIDCMOSWirelessRFIDHigh-RFIDRFIDTrans-architecturespeedceiversensing2Sampling rate 10101000101010(kHz) per sensorMax. Multi-1000:110:15:1100:1200:1plexing ratioData rate needed83 kb/s10 Mb/s10 Mb/s50 kb/s1 Mb/s2 Mb/sRF link 1 × 1 for 10000.25 ×0.71 ×area (mm2)sensors0.450.78Max. Power1~12.61430consumption (μW)Max. Energy12<12.6295per bit (pJ/bit)Flexibility,desiredYes, YesYes, ?No, NoNo, NoBio-compatibility1) Total RF power dissipation of 10 mW/cm2 is used so not to raise the local brain temperature and disturb neural activities.2) The high-speed sensing will be carried out in case of stimulating a sub-group of sensors for higher temporal resolution (0.1 μsec) along the axon.
In order not to disturb neural activities, the maximum allowed local temperature increase is ˜2° C., which limits heat dissipation to ˜40 mW/cm2, as discussed in reference to Table 1, and further described in Reference 14 below. This sets the total power budget of the sensor wireless electronics. Given a power budget to monitor an area of 50×50 μm2 with ˜8 neurons of 1 μW, the transmission energy through a neural wireless link is limited to 12 pJ/bit.
Prior art silicon CMOS transceivers for implantable medical applications have shown 2 Mb/sec On-Off-Key (OOK) communications, but with an excessive total power consumption of 1430 μW and energy per bit of 295 pJ/bit for the receiver, as described in Reference 15 below. Even with the adoption of new envelope demodulator circuits, the overall power dissipation and energy-per-bit is too high, way above the ˜40 mW/cm2 limit. Leveraging advanced CMOS technologies, successive approximation register analog to digital converters (ADCs), as described in Reference 4 below, have also been used with sophisticated digital signal processors to deconstruct neuron signals into amplitude and phase data, and with digital filters to process the detected neuron activities. This approach greatly reduces the data communication bandwidth and simplifies the external reader requirement. However, even with a relatively low processing rate, 64 channels of neuron recording still dissipate ˜1.4 mW
Wirelessly powered radio frequency Identification (RFID) architectures can offer very low energy per bit with ultra-low power operation. Wireless neural sensors have been demonstrated using 65 nm CMOS technology with power consumption as low as 2.6 μW/channel at a 1 Mbit/sec data rate, as described in Reference 16 below. However, the demonstrated wireless neural sensor is invasive and limited in scaling as it uses Si shanks, and also its total required RF power is ˜50 mW.
Another key requirement of the wireless electronics is flexibility and bio-compatibility, which is highly desirable for long-term monitoring of freely-moving animals, eventually including humans. A bio-compatible and flexible implantable RFID was demonstrated using organic (pentacene) transistors, as described in Reference 17 below. Its frequency was limited to 13.56 MHz with a low data rate of 53 kb/sec. A large supply voltage of 18 Volts was needed due to the poor electronic mobility of 0.5 cm2/Vs and the low driving current of the organic transistors. Other bio-compatible and flexible devices such as ZnO thin film transistors (TFTs) also showed very poor electronic mobility of 0.95 cm2/Vs, as described in Reference 18 below.
What is needed is simultaneous recording of the single cell activity of large numbers of neurons over various regions of the brain with high spatial resolution. Also needed is a capability to simultaneously stimulate the neurons and record neural activity by individual electrodes/sensors on the array in order to understand the functional relationships between neurons. The embodiments of the present disclosure address these and other needs.